Analysis of India’s Air Pollution Data

Introduction

In 2019, a group of researchers from UBC Vancouver conducted a field study in India to investigate the relationship between energy usage and air quality experienced by rural communities. This project aims to assess the effects of conventional household-level energy usage on indoor air pollution levels in rural areas. We focus on characterizing cooking emissions by examining real time concentrations of pollutants like \(PM_{2.5}\) and Black Carbon (BC).

Indoor Air Exposure

  • Use of Solid Biomass Fuel (SBF) in cooking can emit \(PM_{2.5}\), Black Carbon and CO in indoor spaces
  • SBF use is common in Low and Middle income countries like India, particularly the rural households (which can account for 80% of total population)
  • Cooking Emission can be as high as 20 times of the background concentration
  • Factors that effect the exposure to indoor pollutants
    • Fuel usage in cooking
    • Type of cooking setup (oven type and kitchen type)
    • Household ventilation
    • Food choices and cooking time
  • Primary emission reduction can be achieved by adopting clean fuels such as Liquefied Petroleum Gas (LPG)
  • However, affordability and fuel-stacking remains a big challenge
  • Community and household level actionable measures can be a solution to reduce exposure
  • Additionally, measuring Black Carbon concentration in rural setup can be very challenging and hence very limited Black Carbon monitoring data are available from rural communities.

Black Carbon (BC) - Measurement and Challanges

  • BC is a component of combustion-derived particulate matter (PM)
  • BC is a strong absorber of solar radiation, with 20 years Global Warming Potential of 4470
  • BC has also been identified as a toxic pollutant - can impact human organ growth
  • However, monitoring BC is a big challenge as there is no such particular chemical definition
  • Present BC measurement technique involves an Aethalometer, which operates on the continuous measurement of light absorption.
  • Latest multi-wavelength Aethalometers can be used to identify the source of BC by performing source apportionment analysis.
  • A parameter that can explain the strength of spectral light absorption, Absorption Angstrom Exponent (AAE), is often used to identify the source of BC.
  • Typically, AAE of BC from fossil fuel-based sources is considered to be 1. The presence of biomass-based BC sources can cause enhanced light absorption in lower wavelengths and increase AAE values.

Effect of Wildfire smoke on AAE values

In 2020 summer, Vancouver’s lower mainland experienced ten days of wildfire smoke between August and November. The continuous Aethalometer measurements performed at a near-road monitoring site show increased AAE values (\(AAE_{WF} = 1.71\)) during the wildfire-affected days. For non-wildfire days, average AAE (\(AAE_{Reg}\)) values were estimated as 1.23.

AAE (power law fit of light absorption) from Vancouver's Data

AAE (power law fit of light absorption) from Vancouver’s Data

Campaign in India

  • This study focused on characterizing emissions experienced by the rural household in India
  • We performed community-level monitoring (both indoor and outdoor) of air pollution in two villages in northern India.
  • RAMP (Real-time, Affordable, Multi-Pollutant) air quality monitors were used to measure air pollutants: carbon monoxide (\(CO\)), nitrogen dioxide (\(NO_2\)), ozone (\(O_3\)), and fine particulate mass (\(PM_{2.5}\)).
  • Micro-Aethalometers (model: MA300) are portable, battery-powered aethalometer and were used to measure black carbon concentration in this campaign.
  • Participant survey conducted to learn about their fuel usage patterns, cooking practices
  • Participants’ building structures were studied to understand the state of household ventilation.
Pollution Monitoring in Indian VillagesPollution Monitoring in Indian Villages

Pollution Monitoring in Indian Villages

Location Details

  • Indo-Gangetic Plain (IGP) has been in the limelight for being the hotspot of intense air pollution in Northern India.
  • This study has been conducted in rural regions of Unnao district in Uttar Pradesh, located at the heart of IGP.
  • Rural Monitoring Sites
    • Village 1: Bhawani Kheda (BK)
    • Village 2: Naikani Kheda (NK)
  • Regulatory Monitoring Sites
    • We also identify a few continuous air quality monitoring stations close to our campaign location operated by Central Pollution Control Board of India and University of Gothenburg.
    • These stations are equipped with regulatory-grade monitoring instruments, and data from these stations can help validate low-cost sensors’ performance.

Air Quality Monitoring Locations and Map

Station ID Location Operated by Monitoring Type Site Type
BK Unnao University of British Columbia Low Cost Network Rural
NK Unnao University of British Columbia Low Cost Network Rural
KNP Kanpur Central Pollution Control Board, Kanpur Regulatory Grade Urban
LKO_Sch Lucknow Central Pollution Control Board, Lucknow Regulatory Grade Urban
LKO_Ind Lucknow Central Pollution Control Board, Lucknow Regulatory Grade Industrial
HAMI Hamirpur University of Gothenburg Regulatory Grade Rural

Household and Village Details

  • In this campaign, we monitored indoor air quality for 14 households and surveyed 30 households
  • However, this study focuses on two households from each village.
  • During this campaign, real-time indoor and outdoor pollutant concentrations were measured

Household Structure

  • For the two households, we studied the building structures as variations in kitchen location, openings can lead to differences in household ventilation.
  • Indoor-outdoor exchange of pollutants can contribute to differences in indoor exposure.
Household StructureHousehold Structure

Household Structure

Survey Responses from households

  • Surveys were conducted to know about the families, cooking practices.
  • We identify that both the households were subscribers of Liquefied Petroleum Gas (LPG). However, they are not regular users.
  • Households preferred cooking with SBF.
Parameter Village 1 Household Village 2 Household
Population Adult 2, Children 0 Adult 5, Children 2
Primary Cooking Fuel Dung Cake Dung Cake
Secondary Cooking Fuel Kerosene/Firewood Kerosene/Diesel
Kitchen Type Indoor Outdoor Enclosed Kitchen
LPG Connection Yes Yes
No. Meals 2 2

Exploration of Air Quality Data

  • Parameters Utilized in this work:
    • RAMP: \(PM_{2.5}\), CO, Temperature (T), Relative Humidity (RH)
    • MA300: BC, \(BC_{bb}\) (biomass burning component of BC), \(BC_{ff}\) (fossil fuel component of BC), AAE (Angstrom Exponent)

Pollutant Summary Statisics from Villages

Statistical summary (mean and standard deviation in \(\mu g/m^3\)) of measured BC and \(PM_{2.5}\) during the campaign.

House variable n mean sd
Village1_Outdoor BC 2021 16.45 8.59
Village1_Indoor BC 1934 16.32 6.71
Village2_Outdoor BC 3753 14.80 7.58
Village2_Indoor BC 3223 14.12 7.23
Village1_Outdoor PM2.5 2021 61.37 30.78
Village1_Indoor PM2.5 1428 64.20 28.23
Village2_Outdoor PM2.5 3139 59.75 30.59
Village2_Indoor PM2.5 2036 63.43 44.91

Regional Pollution Profile from CPCB Monitors

  • From the nearest CPCB monitoring stations, we identified regional profiles of particulate pollution.
  • Events like Diwali and Choti Diwali (aka. Naraka Chaturdashi) involve burning candles, earthen lanterns and firecrackers, which can deteriorate outdoor air quality.

Black Carbon data from MA300

  • Real-time estimates of BC can provide insights of cooking and fuel usage patterns
  • MA300 reported data can be utilized to qualitatively understand the fuel mix by applying source apportionment methods.
  • Based on the survey responses, we divided the daily measurement in three Activity sessions to understand the patterns in pollution exposure
    • MrngCook: Morning cooking session (5:00 AM to 9:00 AM)
    • EvngCook: Evening cooking session (5:00 PM to 8:00 PM)
    • NoCook: Hours with no cooking sessions

How many Activity events were captured?

Pollutant concentration by activity period

  • Box plots can be effective in understanding the range and variability of pollutant concentration from the campaign.

Black Carbon Variation

  • Hourly average BC concentration has been utilized to prepare this box plot.
  • Yellow points in these plots reflect the mean concentrations from the campaign.
  • Effect of cooking emission on BC concentration can be visualized during Morning and Evening cooking sessions: indoor BC concentrations were higher than outdoors.
  • Lowest exposure to BC was identified during hours with the no-cooking session.
  • Highest variability in BC concentration were seen during no-cooking hours outdoors due to effect of local meteorological effect and during morning cooking session indoors due to variations in cooking practices (meal variations)

Variation in \(PM_{2.5}\)

It is clear that, morning cooking session can cause extreme exposure to \(PM_{2.5}\). Prolonged exposure to such exposure have been attributed to cardiovascular diseases, respiratory illness, and premature death.

Diurnal Variation of pollutants

Diurnal plots (i.e. hourly variation) of BC and \(PM_{2.5}\). The red line represents the mean indoor concentration, and the blue line represents the mean outdoor concentration. The shaded region represents the 95% confidence interval around the mean estimates.

  • Height of the planetary boundary layer (PBL) has been identified as a critical factor in the vertical mixing and dilution of near-surface pollutants.
  • During afternoon hours, PBL height increases and reduces pollutant concentration.
  • Peak concentrations coincided with the cooking hours, both indoor and outdoor.
  • Maximum variability in pollutant concentrations was observed during peak concentrations, reflecting variations in the exposure to pollutants.
  • During cooking sessions, SBF’s usage contributed to a sharp increase in indoor BC concentrations.
  • Slight lag in peak concentrations was identified for outdoor BC concentrations, reflecting indoor to outdoor exchange of BC-enriched aerosols.

Black Carbon Source Apportionment Results

Here, I show the apportioned BC components. Using the Aethalometer source apportionment model, total BC concentration can be divided into two segments: \(BC_{bb}\) and \(BC_{ff}\). BC originated from biomass-burning-based sources (such as SBF) can be attributed to \(BC_{bb}\), and \(BC_{ff}\) can be attributed to BC mass from fossil fuel sources (such as kerosene, diesel). Notice that \(BC_{bb}\) dominated over the day when compared against \(BC_{ff}\).

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